Abstract
Modern colorization techniques can create artificially-colorized images that are indistinguishable from natural color images. As a result, the detection of fake colorized images is attracting the interest of the digital forensics research community. This chapter tackles the challenge by introducing a detection approach that leverages neural networks. It analyzes the statistical differences between fake colorized images and their corresponding natural images, and shows that significant differences exist. A simple, but effective, feature extraction technique is proposed that utilizes cosine similarity to measure the overall similarity of normalized histogram distributions of various channels for natural and fake images. A special neural network with a simple structure but good performance is trained to detect fake colorized images. Experiments with datasets containing fake colorized images generated by three state-of-the-art colorization techniques demonstrate the performance and robustness of the proposed approach.
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K. Bahrami, A. Kot, L. Li and H. Li, Blurred image splicing localization by exposing blur type inconsistency, IEEE Transactions on Information Forensics and Security, vol. 10(5), pp. 999–1009, 2015.
R. Bohme and M. Kirchner, Counter-forensics: Attacking image forensics, in Digital Image Forensics, H. Sencar and N. Memon (Eds.), Springer, New York, pp. 327–366, 2013.
C. Chang and C. Lin, LIBSVM: A library for support vector machines, ACM Transactions on Intelligent Systems and Technology, vol. 2(3), article no. 27, 2011.
H. Farid, Creating and Detecting Doctored and Virtual Images: Implications to The Child Pornography Prevention Act, Technical Report TR2004-518, Department of Computer Science, Dartmouth College, Hanover, New Hampshire, 2004.
H. Farid, Exposing digital forgeries from JPEG ghosts, IEEE Transactions on Information Forensics and Security, vol. 4(1), pp. 154–160, 2009.
H. Farid, Image forgery detection, IEEE Signal Processing, vol. 26(2), pp. 16–25, 2009.
G. Fasano and A. Franceschini, A multidimensional version of the Kolmogorov-Smirnov test, Monthly Notices of the Royal Astronomical Society, vol. 225(1), pp. 155–170, 1987.
Y. Guo, X. Cao, W. Zhang and R. Wang, Fake colorized image detection, IEEE Transactions on Information Forensics and Security, vol. 13(8), pp. 1932–1944, 2018.
K. He, X. Zhang, S. Ren and J. Sun, Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification, Proceedings of the IEEE International Conference on Computer Vision, pp. 1026–1034, 2015.
G. Huang, Z. Liu, L. van der Maaten and K. Weinberger, Densely connected convolutional networks, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2261–2269, 2017.
S. Iizuka, E. Simo-Serra and H. Ishikawa, Let there be color! Joint end-to-end learning of global and local image priors for automatic image colorization with simultaneous classification, ACM Transactions on Graphics, vol. 35(4), article no. 110, 2016.
R. Ironi, D. Cohen-Or and D. Lischinski, Colorization by example, Proceedings of the Sixteenth Eurographics Conference on Rendering Techniques, pp. 201–210, 2005.
G. Larsson, M. Maire and G. Shakhnarovich, Learning representations for automatic colorization, Proceedings of the Fourteenth European Conference on Computer Vision, Part IV, pp. 577–593, 2016.
Y. LeCun, Y. Bengio and G. Hinton, Deep learning, Nature, vol. 521(7553), pp. 436–444, 2015.
Y. LeCun, B. Boser, J. Denker, D. Henderson, R. Howard, W. Hubbard and L. Jackel, Backpropagation applied to handwritten ZIP code recognition, Neural Computation, vol. 1(4), pp. 541–551, 1989.
A. Levin, D. Lischinski and Y. Weiss, Colorization using optimization, ACM Transactions on Graphics, vol. 23(3), pp. 689–694, 2004.
Q. Luan, F. Wen, D. Cohen-Or, L. Liang, Y. Xu and H. Shum, Natural image colorization, Proceedings of the Eighteenth Eurographics Conference on Rendering Techniques, pp. 309–320, 2007.
E. Reinhard, M. Adhikhmin, B. Gooch and P. Shirley, Color transfer between images, IEEE Computer Graphics and Applications, vol. 21(5), pp. 34–41, 2001.
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. Berg and F. Li, ImageNet Large-Scale Visual Recognition Challenge, International Journal of Computer Vision, vol. 115(3), pp. 211–252, 2015.
D. Sykora, J. Dingliana and S. Collins, LazyBrush: Flexible painting tool for hand-drawn cartoons, Computer Graphics Forum, vol. 28(2), pp. 599–608, 2009.
Y. Wen, K. Zhang, Z. Li and Y. Qiao, A discriminative feature learning approach for deep face recognition, Proceedings of the Fourteenth European Conference on Computer Vision, Part VII, pp. 499–515, 2016.
R. Zhang, P. Isola and A. Efros, Colorful image colorization, Proceedings of the Fourteenth European Conference on Computer Vision, Part III, pp. 649–666, 2016.
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Li, Y., Zhang, Y., Lu, L., Jia, Y., Liu, J. (2019). Using Neural Networks for Fake Colorized Image Detection. In: Peterson, G., Shenoi, S. (eds) Advances in Digital Forensics XV. DigitalForensics 2019. IFIP Advances in Information and Communication Technology, vol 569. Springer, Cham. https://doi.org/10.1007/978-3-030-28752-8_11
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